When we watch a composite figure consisting of two patterns or more, we can segment it into individual patterns, and recognize each pattern separately. Even if one of the patterns to which we are paying attention is affected by noise or defects, we can recall the complete pattern from which the noise has been eliminated and the defects restored. It is not necessary for perfect recall that the stimulus pattern is identical in shape to the pattern which we learned. Even though the pattern is distorted in shape or changed in size, we can recognize it and restore defects by interpolation. During the process of interpolation, we make full use of even slight traces in the defective parts of the pattern to which we are selectively paying attention, and recall the perfect original pattern. A hierarchical neural network model which performs this function of the human brain is proposed in the prior art.
As for a network required for recalling or recognizing a complete pattern on the basis of an incomplete pattern, various kinds of autoassociative recall type associative memory networks have been conventionally proposed. However, most of these conventional associative memory networks do not work well unless the stimulus pattern is identical in size, shape and even position to a training pattern. That is, it could be satisfactorily operated only when an input pattern did not only consist of a previously learned training pattern, but also the former completely coincided with the latter in size, shape and position. Although an associative memory system such as those employing an autocorrelation function accepting only the shift in position of the input pattern has been conventionally proposed, this conventional system also was completely impotent with regard to changes in size and distortion in shape of the pattern.
The present applicant has already disclosed a pattern recognition system called "neocognitron" in the specifications of Japanese Patent No. 1221756 (Japanese Patent Application Publication No. 58(1983)-53790) "A pattern recognition system" and Japanese Patent No. 1279063 (Japanese Patent Application Publication No. 60(1985)-712) "A pattern recognition equipment", which system is provided with an ability for correctly recognizing a pattern despite the effects mentioned above, that is, shifts in position, distortion in shape, and change in size and the like of the input pattern.
However, these disclosed systems and equipment relate to no more than pattern recognition restricted within the above-mentioned version, and hence have not yet been provided with the ability to derive information regarding a phenomenon from incomplete or ambiguous informations with regard thereto by autoassociative recall, that is, the associative memory function required for attaining further higher grade pattern recognition.
On the other hand, the present applicant has also disclosed, as a system provided with the aforesaid associative memory function, a hierarchical information processing network in Japanese Patent Application Laid-open Publication No. 59-163679 "A hierarchical information processing network", which network is provided with the ability to successively select similar patterns resembling a previously learned training pattern in turn from plural input patterns, or, to recall a complete pattern from an incomplete input pattern or an input pattern obscured by noise. However, this hierarchical information processing network has not yet been provided with the aforesaid ability for processing shifts in position and deformations in shape of the input pattern, similarly as the conventional associative memory systems which have been proposed.